uh.bu: Denoising via bottom-up Unbalanced Haar

In unbalhaar: Function Estimation via Unbalanced Haar Wavelets

Denoising via bottom-up Unbalanced Haar

Description

Given an input vector of the form “signal + iid Gaussian noise”, the function estimates the noise level via Median Absolute Deviation, finds the best bottom-up Unbalanced Haar decomposition, thresholds it with the universal threshold, and performs the inverse Unbalanced Haar transform to yield an estimate of the signal.

Usage

uh.bu(x, stretch = length(x))

Arguments

x	a vector of the form “signal + iid Gaussian noise”
stretch	at each iteration, only the first 1:stretch elements of the current input vector (whose length decreases by one with each iteration) get scanned in the search for the worst-fitting fine-scale Unbalanced Haar wavelet

Value

an estimate of the signal

Author(s)

Piotr Fryzlewicz

References

P. Fryzlewicz (2007) “Unbalanced Haar technique for nonparametric function estimation”. Journal of the American Statistical Association, 102, 1318-1327.

See Also

uh, best.unbal.haar.bu, hard.thresh.bu, reconstr.bu

Examples

x <- c(rep(0, 100), rep(1, 200)) + rnorm(300)
est <- uh.bu(x)

unbalhaar documentation built on April 19, 2022, 5:05 p.m.